29 citations · 69 across the 15 of their papers we have counts for
7 papers · 1 filter
MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval
Junjie Zhou, Zheng Liu, Ze Liu +6
Despite the rapidly growing demand for multimodal retrieval, progress in this field remains severely constrained by a lack of training data. In this paper, we introduce MegaPairs,…
Emu3: Next-Token Prediction is All You Need
Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo +22
While next-token prediction is considered a promising path towards artificial general intelligence, it has struggled to excel in multimodal tasks, which are still dominated by diff…
OmniGen: Unified Image Generation
Shitao Xiao, Yueze Wang, Junjie Zhou +7
The emergence of Large Language Models (LLMs) has unified language generation tasks and revolutionized human-machine interaction. However, in the realm of image generation, a unifi…
DenseFusion-1M: Merging Vision Experts for Comprehensive Multimodal Perception
Xiaotong Li, Fan Zhang, Haiwen Diao +3
Existing Multimodal Large Language Models (MLLMs) increasingly emphasize complex understanding of various visual elements, including multiple objects, text information, and spatial…
Unveiling Encoder-Free Vision-Language Models
Haiwen Diao, Yufeng Cui, Xiaotong Li +3
Existing vision-language models (VLMs) mostly rely on vision encoders to extract visual features followed by large language models (LLMs) for visual-language tasks. However, the vi…
Unveiling the Ignorance of MLLMs: Seeing Clearly, Answering Incorrectly
Yexin Liu, Zhengyang Liang, Yueze Wang +8
Multimodal Large Language Models (MLLMs) have displayed remarkable performance in multi-modal tasks, particularly in visual comprehension. However, we reveal that MLLMs often gener…
Efficient Multimodal Learning from Data-centric Perspective
Muyang He, Yexin Liu, Boya Wu +4
Multimodal Large Language Models (MLLMs) have demonstrated notable capabilities in general visual understanding and reasoning tasks. However, their deployment is hindered by substa…